Seattle Bikes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "counts = pd.read_csv(\"data/Fremont_Bridge_Bicycle_Counter.csv\", index_col=\"Date\", parse_dates=True)\n",
    "weather = pd.read_csv(\"data/BicycleWeather.csv\", index_col=\"DATE\", parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fremont Bridge Total</th>\n",
       "      <th>Fremont Bridge East Sidewalk</th>\n",
       "      <th>Fremont Bridge West Sidewalk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2012-10-03 00:00:00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-03 01:00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-03 02:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-03 03:00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-03 04:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Fremont Bridge Total  Fremont Bridge East Sidewalk  \\\n",
       "Date                                                                      \n",
       "2012-10-03 00:00:00                  13.0                           4.0   \n",
       "2012-10-03 01:00:00                  10.0                           4.0   \n",
       "2012-10-03 02:00:00                   2.0                           1.0   \n",
       "2012-10-03 03:00:00                   5.0                           2.0   \n",
       "2012-10-03 04:00:00                   7.0                           6.0   \n",
       "\n",
       "                     Fremont Bridge West Sidewalk  \n",
       "Date                                               \n",
       "2012-10-03 00:00:00                           9.0  \n",
       "2012-10-03 01:00:00                           6.0  \n",
       "2012-10-03 02:00:00                           1.0  \n",
       "2012-10-03 03:00:00                           3.0  \n",
       "2012-10-03 04:00:00                           1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>SNWD</th>\n",
       "      <th>SNOW</th>\n",
       "      <th>TMAX</th>\n",
       "      <th>TMIN</th>\n",
       "      <th>AWND</th>\n",
       "      <th>WDF2</th>\n",
       "      <th>WDF5</th>\n",
       "      <th>...</th>\n",
       "      <th>WT17</th>\n",
       "      <th>WT05</th>\n",
       "      <th>WT02</th>\n",
       "      <th>WT22</th>\n",
       "      <th>WT04</th>\n",
       "      <th>WT13</th>\n",
       "      <th>WT16</th>\n",
       "      <th>WT08</th>\n",
       "      <th>WT18</th>\n",
       "      <th>WT03</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>GHCND:USW00024233</td>\n",
       "      <td>SEATTLE TACOMA INTERNATIONAL AIRPORT WA US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>50</td>\n",
       "      <td>47</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>GHCND:USW00024233</td>\n",
       "      <td>SEATTLE TACOMA INTERNATIONAL AIRPORT WA US</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>28</td>\n",
       "      <td>45</td>\n",
       "      <td>180</td>\n",
       "      <td>200</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      STATION                                STATION_NAME  \\\n",
       "DATE                                                                        \n",
       "2012-01-01  GHCND:USW00024233  SEATTLE TACOMA INTERNATIONAL AIRPORT WA US   \n",
       "2012-01-02  GHCND:USW00024233  SEATTLE TACOMA INTERNATIONAL AIRPORT WA US   \n",
       "\n",
       "            PRCP  SNWD  SNOW  TMAX  TMIN  AWND  WDF2  WDF5  ...  WT17  WT05  \\\n",
       "DATE                                                        ...               \n",
       "2012-01-01     0     0     0   128    50    47   100    90  ... -9999 -9999   \n",
       "2012-01-02   109     0     0   106    28    45   180   200  ... -9999 -9999   \n",
       "\n",
       "            WT02  WT22  WT04  WT13  WT16  WT08  WT18  WT03  \n",
       "DATE                                                        \n",
       "2012-01-01 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999  \n",
       "2012-01-02 -9999 -9999 -9999     1     1 -9999 -9999 -9999  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily = counts.resample(\"d\").sum()\n",
    "daily[\"Total\"] = daily.sum(axis=1)\n",
    "daily = daily[[\"Total\"]]\n",
    "# no hay datos de precipitacion luego de mayo 2014 en el dataset de weather\n",
    "daily = daily[:\"2015-5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\n",
    "for i in range(7):\n",
    "    daily[days[i]] = (daily.index.dayofweek == i).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2012-10-03</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-04</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>6296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-06</td>\n",
       "      <td>4012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-07</td>\n",
       "      <td>4284.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun\n",
       "Date                                                 \n",
       "2012-10-03  7042.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0\n",
       "2012-10-04  6950.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0\n",
       "2012-10-05  6296.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0\n",
       "2012-10-06  4012.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0\n",
       "2012-10-07  4284.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "cal = USFederalHolidayCalendar()\n",
    "holidays = cal.holidays(\"2012\", \"2016\")\n",
    "daily = daily.join(pd.Series(1, index=holidays, name=\"holiday\"))\n",
    "daily[\"holiday\"] = daily[\"holiday\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>holiday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2012-10-03</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-04</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>6296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-06</td>\n",
       "      <td>4012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-07</td>\n",
       "      <td>4284.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-08</td>\n",
       "      <td>7074.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun  holiday\n",
       "Date                                                          \n",
       "2012-10-03  7042.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0\n",
       "2012-10-04  6950.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0\n",
       "2012-10-05  6296.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0\n",
       "2012-10-06  4012.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0\n",
       "2012-10-07  4284.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0\n",
       "2012-10-08  7074.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0      1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def hours_of_daylight(date, axis=23.44, latitude=47.61):\n",
    "    \"\"\"Daylight hours for given date\"\"\"\n",
    "    days = (date - pd.datetime(2000, 12, 21)).days\n",
    "    m = (1. - np.tan(np.radians(latitude))\n",
    "        * np.tan(np.radians(axis) * np.cos(days * 2 * np.pi / 365.25)))\n",
    "    return 24. * np.degrees(np.arccos(1 - np.clip(m, 0, 2))) / 180.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily[\"daylight_hrs\"] = list(map(hours_of_daylight, daily.index))\n",
    "daily[\"daylight_hrs\"].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
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       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
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       "      <th>Date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2012-10-03</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.277359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-04</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.219142</td>\n",
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       "    <tr>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>6296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.161038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-06</td>\n",
       "      <td>4012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.103056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-07</td>\n",
       "      <td>4284.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.045208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun  holiday  daylight_hrs\n",
       "Date                                                                        \n",
       "2012-10-03  7042.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0     11.277359\n",
       "2012-10-04  6950.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0     11.219142\n",
       "2012-10-05  6296.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0     11.161038\n",
       "2012-10-06  4012.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0     11.103056\n",
       "2012-10-07  4284.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0     11.045208"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather[\"TMIN\"] /= 10\n",
    "weather[\"TMAX\"] /= 10\n",
    "weather[\"Temp (C)\"] = 0.5 * (weather[\"TMIN\"] + weather[\"TMAX\"])\n",
    "\n",
    "weather[\"dry day\"] = (weather[\"PRCP\"] == 0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily = daily.join(weather[[\"PRCP\", \"Temp (C)\", \"dry day\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2012-10-03</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.277359</td>\n",
       "      <td>0</td>\n",
       "      <td>13.35</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>2012-10-04</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.219142</td>\n",
       "      <td>0</td>\n",
       "      <td>13.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>6296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.161038</td>\n",
       "      <td>0</td>\n",
       "      <td>15.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-06</td>\n",
       "      <td>4012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.103056</td>\n",
       "      <td>0</td>\n",
       "      <td>15.85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-07</td>\n",
       "      <td>4284.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.045208</td>\n",
       "      <td>0</td>\n",
       "      <td>15.85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-08</td>\n",
       "      <td>7074.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.987503</td>\n",
       "      <td>0</td>\n",
       "      <td>14.45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-09</td>\n",
       "      <td>7002.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.929950</td>\n",
       "      <td>0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>6470.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.872560</td>\n",
       "      <td>0</td>\n",
       "      <td>10.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-11</td>\n",
       "      <td>6094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.815345</td>\n",
       "      <td>0</td>\n",
       "      <td>10.55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-12</td>\n",
       "      <td>4022.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.758314</td>\n",
       "      <td>20</td>\n",
       "      <td>11.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-13</td>\n",
       "      <td>1532.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.701479</td>\n",
       "      <td>48</td>\n",
       "      <td>13.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-14</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.644852</td>\n",
       "      <td>165</td>\n",
       "      <td>15.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-15</td>\n",
       "      <td>4546.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.588444</td>\n",
       "      <td>79</td>\n",
       "      <td>14.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-16</td>\n",
       "      <td>6072.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.532267</td>\n",
       "      <td>0</td>\n",
       "      <td>12.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-17</td>\n",
       "      <td>6486.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.476334</td>\n",
       "      <td>0</td>\n",
       "      <td>10.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-18</td>\n",
       "      <td>5846.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.420659</td>\n",
       "      <td>208</td>\n",
       "      <td>12.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>3954.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.365253</td>\n",
       "      <td>48</td>\n",
       "      <td>12.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-20</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.310130</td>\n",
       "      <td>5</td>\n",
       "      <td>8.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-21</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.255305</td>\n",
       "      <td>64</td>\n",
       "      <td>8.05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-22</td>\n",
       "      <td>4258.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.200791</td>\n",
       "      <td>89</td>\n",
       "      <td>5.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun  holiday  daylight_hrs  \\\n",
       "Date                                                                           \n",
       "2012-10-03  7042.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0     11.277359   \n",
       "2012-10-04  6950.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0     11.219142   \n",
       "2012-10-05  6296.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0     11.161038   \n",
       "2012-10-06  4012.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0     11.103056   \n",
       "2012-10-07  4284.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0     11.045208   \n",
       "2012-10-08  7074.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0      1.0     10.987503   \n",
       "2012-10-09  7002.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0      0.0     10.929950   \n",
       "2012-10-10  6470.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0     10.872560   \n",
       "2012-10-11  6094.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0     10.815345   \n",
       "2012-10-12  4022.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0     10.758314   \n",
       "2012-10-13  1532.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0     10.701479   \n",
       "2012-10-14  1396.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0     10.644852   \n",
       "2012-10-15  4546.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0      0.0     10.588444   \n",
       "2012-10-16  6072.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0      0.0     10.532267   \n",
       "2012-10-17  6486.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0     10.476334   \n",
       "2012-10-18  5846.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0     10.420659   \n",
       "2012-10-19  3954.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0     10.365253   \n",
       "2012-10-20  2136.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0     10.310130   \n",
       "2012-10-21  1978.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0     10.255305   \n",
       "2012-10-22  4258.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0      0.0     10.200791   \n",
       "\n",
       "            PRCP  Temp (C)  dry day  \n",
       "Date                                 \n",
       "2012-10-03     0     13.35        1  \n",
       "2012-10-04     0     13.60        1  \n",
       "2012-10-05     0     15.30        1  \n",
       "2012-10-06     0     15.85        1  \n",
       "2012-10-07     0     15.85        1  \n",
       "2012-10-08     0     14.45        1  \n",
       "2012-10-09     0     12.50        1  \n",
       "2012-10-10     0     10.25        1  \n",
       "2012-10-11     0     10.55        1  \n",
       "2012-10-12    20     11.40        0  \n",
       "2012-10-13    48     13.90        0  \n",
       "2012-10-14   165     15.55        0  \n",
       "2012-10-15    79     14.15        0  \n",
       "2012-10-16     0     12.20        1  \n",
       "2012-10-17     0     10.25        1  \n",
       "2012-10-18   208     12.25        0  \n",
       "2012-10-19    48     12.20        0  \n",
       "2012-10-20     5      8.60        0  \n",
       "2012-10-21    64      8.05        0  \n",
       "2012-10-22    89      5.55        0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>holiday</th>\n",
       "      <th>daylight_hrs</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>Temp (C)</th>\n",
       "      <th>dry day</th>\n",
       "      <th>annual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2012-10-03</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.277359</td>\n",
       "      <td>0</td>\n",
       "      <td>13.35</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-04</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.219142</td>\n",
       "      <td>0</td>\n",
       "      <td>13.60</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>6296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.161038</td>\n",
       "      <td>0</td>\n",
       "      <td>15.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-06</td>\n",
       "      <td>4012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.103056</td>\n",
       "      <td>0</td>\n",
       "      <td>15.85</td>\n",
       "      <td>1</td>\n",
       "      <td>0.008219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-10-07</td>\n",
       "      <td>4284.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.045208</td>\n",
       "      <td>0</td>\n",
       "      <td>15.85</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun  holiday  daylight_hrs  \\\n",
       "Date                                                                           \n",
       "2012-10-03  7042.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0     11.277359   \n",
       "2012-10-04  6950.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0     11.219142   \n",
       "2012-10-05  6296.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0     11.161038   \n",
       "2012-10-06  4012.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0     11.103056   \n",
       "2012-10-07  4284.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0     11.045208   \n",
       "\n",
       "            PRCP  Temp (C)  dry day    annual  \n",
       "Date                                           \n",
       "2012-10-03     0     13.35        1  0.000000  \n",
       "2012-10-04     0     13.60        1  0.002740  \n",
       "2012-10-05     0     15.30        1  0.005479  \n",
       "2012-10-06     0     15.85        1  0.008219  \n",
       "2012-10-07     0     15.85        1  0.010959  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily[\"annual\"] = (daily.index - daily.index[0]).days / 365.\n",
    "daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>holiday</th>\n",
       "      <th>daylight_hrs</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>Temp (C)</th>\n",
       "      <th>dry day</th>\n",
       "      <th>annual</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Total, Mon, Tue, Wed, Thu, Fri, Sat, Sun, holiday, daylight_hrs, PRCP, Temp (C), dry day, annual, predicted]\n",
       "Index: []"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# revisar si hay Nan antes de fitear el modelo\n",
    "daily[daily.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "column_names = [\"Mon\", \"Tue\",\"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\", \n",
    "                \"holiday\", \"daylight_hrs\", \"PRCP\", \"Temp (C)\", \n",
    "                \"dry day\", \"annual\"]\n",
    "X = daily[column_names]\n",
    "y = daily[\"Total\"]\n",
    "\n",
    "model = LinearRegression(fit_intercept=False)\n",
    "model.fit(X, y)\n",
    "daily[\"predicted\"] = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e9e1e7150>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily[[\"predicted\", \"Total\"]].plot(alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mon              807.438753\n",
       "Tue              959.748149\n",
       "Wed              918.408231\n",
       "Thu              711.906714\n",
       "Fri              153.111859\n",
       "Sat            -2335.624689\n",
       "Sun            -2409.814578\n",
       "holiday        -2302.232290\n",
       "daylight_hrs     253.609836\n",
       "PRCP              -5.268074\n",
       "Temp (C)         144.042831\n",
       "dry day         1102.743912\n",
       "annual           155.233064\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = pd.Series(model.coef_, index=X.columns)\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "np.random.seed(1)\n",
    "err = np.std([model.fit(*resample(X, y)).coef_ for i in range(1000)], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>effect</th>\n",
       "      <th>err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Mon</td>\n",
       "      <td>807.0</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tue</td>\n",
       "      <td>960.0</td>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wed</td>\n",
       "      <td>918.0</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Thu</td>\n",
       "      <td>712.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fri</td>\n",
       "      <td>153.0</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sat</td>\n",
       "      <td>-2336.0</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sun</td>\n",
       "      <td>-2410.0</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>holiday</td>\n",
       "      <td>-2302.0</td>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>daylight_hrs</td>\n",
       "      <td>254.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PRCP</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Temp (C)</td>\n",
       "      <td>144.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>dry day</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>annual</td>\n",
       "      <td>155.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              effect    err\n",
       "Mon            807.0  181.0\n",
       "Tue            960.0  175.0\n",
       "Wed            918.0  173.0\n",
       "Thu            712.0  185.0\n",
       "Fri            153.0  174.0\n",
       "Sat          -2336.0  180.0\n",
       "Sun          -2410.0  179.0\n",
       "holiday      -2302.0  325.0\n",
       "daylight_hrs   254.0   19.0\n",
       "PRCP            -5.0    0.0\n",
       "Temp (C)       144.0    7.0\n",
       "dry day       1103.0   70.0\n",
       "annual         155.0   37.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"effect\": params.round(0), \"err\": err.round(0)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8685664519228127"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "column_names = [\"Mon\", \"Tue\",\"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\", \n",
    "                \"holiday\", \"daylight_hrs\", \"PRCP\", \"Temp (C)\", \n",
    "                \"dry day\", \"annual\"]\n",
    "X = daily[column_names]\n",
    "y = daily[\"Total\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "\n",
    "model = LinearRegression(fit_intercept=False)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "daily[\"predicted\"] = model.predict(X)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e9366e390>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily[[\"predicted\"]].plot(alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e935268d0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily[[\"Total\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}